What is a thread?

A thread is a unit of execution inside a process.

• One process can have multiple threads.
• Threads share the same memory of the process.

When to use threading

Threading is best for I/O-bound tasks:

• network requests
• reading/writing files
• waiting on APIs

When NOT to use threading

For CPU-bound tasks (heavy computation), Python threads are limited by the GIL (Global Interpreter Lock) in CPython.

For CPU-bound parallelism, prefer:

• multiprocessingmultiprocessing
• native extensions

The Global Interpreter Lock (GIL)

In CPython, only one thread executes Python bytecode at a time.

Practical takeaway:

• Threads can still improve performance when time is spent waiting (I/O).
• Threads won’t speed up CPU-only loops significantly.

Quick demo (I/O wait simulation)

import time
import threading
 
 
def task(i: int) -> None:
    time.sleep(1)
    print("done", i)
 
 
threads = []
start = time.time()
 
for i in range(5):
    t = threading.Thread(target=task, args=(i,))
    t.start()
    threads.append(t)
 
for t in threads:
    t.join()
 
print("seconds:", round(time.time() - start, 2))

import time
import threading
 
 
def task(i: int) -> None:
    time.sleep(1)
    print("done", i)
 
 
threads = []
start = time.time()
 
for i in range(5):
    t = threading.Thread(target=task, args=(i,))
    t.start()
    threads.append(t)
 
for t in threads:
    t.join()
 
print("seconds:", round(time.time() - start, 2))

Without threads, this would take about 5 seconds; with threads it’s ~1 second (because sleep is I/O wait).

🧪 Try It Yourself

Exercise 1 – Create a Simple Thread

Exercise 2 – Join a Thread

Exercise 3 – Run Multiple Threads